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Field
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the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics
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process outputs. The research will be conducted in close collaboration with our industry partner Netherlands Organization for Applied Scientific Research (TNO), and will include combustion experiments in
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design scalable numerical methods for quantum master equations, implement high-performance simulations, and help build open-source tools for large-scale spin-system modeling. By improving our ability
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process models from the field of spatial statistics to model clustered patterns across the landscape, and develop methods for estimating plant population size and/or change. Qualifications: Requirements
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improvements. Develop neuromorphic sensory systems for biomedical and other application domains. Model and simulate neuromorphic devices, circuits and systems. Investigate spike-based signal processing and
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inventorying forest biodiversity. Possible areas include: indicators of functional or taxonomic diversity species-specific or habitat-based monitoring combinations of field data, remote sensing, and modelling
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areas include: indicators of functional or taxonomic diversity species-specific or habitat-based monitoring combinations of field data, remote sensing, and modelling new techniques for detecting and
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. Computational tools for simulating such processes - both traditional based e.g. on computational fluid dynamics and more recent based on AI/machine learning - constitute fundamental scientific domains that act as
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information, for example data derived from remote sensing, use point process models from the field of spatial statistics to model clustered patterns across the landscape, and develop methods for estimating
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in this project. As successful candidate, you will investigate predisposition of trees to drought stress by long-term fertilization, both experimentally and with modelling. You will analyze long-term